In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer

Abstract

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC.

Keywords: co-expressed genes; copy number alterations; microRNA/miRNA; prostate cancer.

Figure 1

Venn diagram for the integrative approaches.

Figure 2

Co-expression gene network of core genes from four gene signatures overlapping with published gene signatures (* four gene signatures).

Figure 3

Gene co-expression network and putative miRNA-regulated targets.

Figure 4

Area Under Curve (AUC) values of six different approaches: green bar (I method with 3069 genes), red bar (II method with 38 genes), blue bar (III method with 21 genes), gray bar (IV method with 4 genes), yellow bar (V method with 19 genes), and pink bar (VI method with hsa-miR-153).

Figure 5

Classification for single gene from the IV approach (four gene signatures): blue bar (CLU gene), red bar (KLF5), gray bar (EPHA3), and yellow bar (TRIB1).

Figure 6

AUC values among three different approaches with dataset TCGA, considering also a subset of random genes. The light green box represents AUC with 3069 random genes, and the dark green box AUC with 3069 genes according to our approach. The orange box represents AUC with 38 random genes, and the red box AUC with 38 genes according to our approach. The light blue box represents AUC with 21 random genes, and the dark blue box AUC with 21 genes according to our approach.

Figure 7

AUC values of six different approaches with a validation dataset GEO (GSE79021 for gene expression, and GSE21036 for miRNA): green bar (I method with 3069 genes), red bar (II method with 38 genes), blue bar (III method with 21 genes), gray bar (IV method with 4 genes), yellow bar (V method with 19 genes), and pink bar (VI method with hsa-miR-153).

Figure 8

Workflow of the proposed analysis.